Switching physical systems are ubiquitous in modern control applications, for instance, locomotion behavior of robots and animals, power converters with switches and diodes. The dynamics and switching conditions are often hard to obtain or even inaccessible in case of a-priori unknown environments and nonlinear components. Black-box neural networks can learn to approximately represent switching dynamics, but typically require a large amount of data, neglect the underlying axioms of physics, and lack of uncertainty quantification. We propose a Gaussian process based learning approach enhanced by switching Port-Hamiltonian systems (GP-SPHS) to learn physical plausible system dynamics and identify the switching condition. The Bayesian nature of Gaussian processes uses collected data to form a distribution over all possible switching policies and dynamics that allows for uncertainty quantification. Furthermore, the proposed approach preserves the compositional nature of Port-Hamiltonian systems. A simulation with a hopping robot validates the effectiveness of the proposed approach.

翻译：切换物理系统在现代控制应用中普遍存在，例如机器人和动物的运动行为、带有开关和二极管的功率转换器。在环境先验未知及存在非线性组件的情况下，系统的动力学特性和切换条件往往难以获取甚至无法获得。黑盒神经网络虽能近似学习切换动力学，但通常需要大量数据，且忽略了物理基本公理，缺乏不确定性量化能力。我们提出一种基于高斯过程的学习方法，通过增强切换端口-哈密顿系统（GP-SPHS）来学习符合物理规律的动力学特性并识别切换条件。高斯过程的贝叶斯特性可利用采集数据构建所有可能切换策略及动力学的分布，从而实现不确定性量化。此外，该方法保留了端口-哈密顿系统的组合特性。通过弹跳机器人仿真验证了所提方法的有效性。